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01 Β· .sql / .py

Data Engineering

Design and operate the pipelines that move data reliably β€” from ingestion to warehouse, at a scale where shortcuts break.

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Batch & streaming pipelines

Apache Spark and Kafka for high-volume, low-latency data movement.

🌬️

Orchestration

Airflow DAGs for scheduling, retries and dependency management.

☁️

Cloud warehousing

AWS (Redshift, S3, Glue) and dbt for transformation layers.

⏱ 14 weeksπŸ‘€ IntermediateπŸ’° β‚Ή8–18 LPA
pipeline.py
1from airflow import DAG
2# daily ingest β†’ transform β†’ load
3extract("kafka://events")
4  .transform("spark_job")
5  .load("redshift.fact_sales")
6schedule = "@daily"
model.ipynb
1from sklearn import ensemble
2model = ensemble.RandomForest()
3model.fit(X_train, y_train)
4# deploy behind a Flask endpoint
5app.route("/predict")
02 Β· .ipynb

Data Science

From statistics and exploratory analysis to deep learning and model deployment β€” the full lifecycle, not just Kaggle notebooks.

πŸ“Š

Statistics & EDA

Hypothesis testing, feature engineering and data storytelling fundamentals.

🧠

ML & deep learning

scikit-learn, TensorFlow and NLP with real, noisy datasets.

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Model deployment

Serve models via Flask APIs and track them post-deployment.

⏱ 16 weeksπŸ‘€ Beginner to ProπŸ’° β‚Ή6–14 LPA
03 Β· .pbix

Data Analytics

The most accessible entry point into data careers β€” SQL, spreadsheets and dashboards that answer real business questions.

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SQL & Excel

Query, clean and shape data confidently before it ever hits a dashboard.

πŸ“ˆ

Power BI & Tableau

Build dashboards stakeholders actually open every week.

🐍

Python for analysis

Pandas fundamentals for the analyses spreadsheets can't handle.

⏱ 12 weeksπŸ‘€ Beginner to ProπŸ’° β‚Ή4–8 LPA
sales_query.sql
1SELECT region, SUM(revenue)
2FROM sales_fact
3WHERE qtr = 'Q1-2026'
4GROUP BY region
5ORDER BY 2 DESC;
rag_chain.py
1from langchain import RAGChain
2retriever = vectordb.as_retriever()
3chain = RAGChain(retriever, llm="gpt-4o")
4chain.run("summarise this contract")
04 Β· .env

Generative AI

Move past prompt tricks β€” build retrieval-augmented applications and understand where LLMs fail.

πŸ’¬

Prompt engineering

Structured prompting techniques that hold up in production, not just demos.

πŸ”—

LangChain & RAG

Build retrieval pipelines that ground answers in your own data.

🧩

AI application dev

Wire LLMs into real applications using the OpenAI and open-source APIs.

⏱ 10 weeksπŸ‘€ All LevelsπŸ’° β‚Ή6–15 LPA
05 Β· .test

ETL Testing

The quality gate every data pipeline needs. Learn to catch bad data before it reaches a dashboard or a decision.

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Data validation

Source-to-target reconciliation and transformation rule testing.

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Warehouse QA

SQL-driven testing across staging, warehouse and reporting layers.

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Tooling

Informatica, DataStage and automation scripts for repeatable checks.

⏱ 10 weeksπŸ‘€ BeginnerπŸ’° β‚Ή4–9 LPA
test_etl.sql
1-- row count reconciliation
2SELECT (SELECT COUNT(*) FROM src) -
3     (SELECT COUNT(*) FROM tgt)
4AS row_diff;
compare

Course comparison at a glance

TRACK DURATION LEVEL SALARY BAND
Data Engineering14 weeksIntermediateβ‚Ή8–18 LPA
Data Science16 weeksBeginner to Proβ‚Ή6–14 LPA
Data Analytics12 weeksBeginner to Proβ‚Ή4–8 LPA
Generative AI10 weeksAll Levelsβ‚Ή6–15 LPA
ETL Testing10 weeksBeginnerβ‚Ή4–9 LPA

Still deciding between two tracks?

A 15-minute call with our counsellor usually settles it.

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